<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"/><title>未知 </title></head><body>
<div align="center">
  <p>
    <a align="center" href="https://ultralytics.com/yolov5" target="_blank">
      <img width="850" src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
  </p>

[è‹±æ–‡](README.md)\|[ç®€ä½“ä¸­æ–‡](README.zh-CN.md)<br>

  <div>
    <a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
    <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
    <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
    <br>
    <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
    <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
  </div>
  <br>

YOLOv5 ğŸš€ æ˜¯ä¸–ç•Œä¸Šæœ€å�—æ¬¢è¿�çš„è§†è§‰ AIï¼Œä»£è¡¨<a href="https://ultralytics.com"> Ultralytics </a>å¯¹æœªæ�¥è§†è§‰ AI æ–¹æ³•çš„å¼€æº�ç ”ç©¶ï¼Œç»“å�ˆåœ¨æ•°å�ƒå°�æ—¶çš„ç ”ç©¶å’Œå¼€å�‘ä¸­ç§¯ç´¯çš„ç»�éªŒæ•™è®­å’Œæœ€ä½³å®�è·µã€‚

å¦‚æ�œè¦�ç”³è¯·ä¼�ä¸šè®¸å�¯è¯�ï¼Œè¯·å¡«å†™è¡¨æ ¼<a href="https://ultralytics.com/license">Ultralytics è®¸å�¯</a>.

  <div align="center">
    <a href="https://github.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-github.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-linkedin.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-twitter.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-producthunt.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-youtube.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-facebook.png" width="2%" alt="" /></a>
    <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="2%" alt="" />
    <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
      <img src="https://github.com/ultralytics/assets/raw/master/social/logo-social-instagram.png" width="2%" alt="" /></a>
  </div>
</div>

<h2 id="ultralytics-c-a14se"><div align="center">Ultralytics ç›´æ’­ä¼šè®®</div></h2>
<div align="center">

[Ultralytics Live Session Ep. 2](https://youtu.be/QGRtEG7UjtE) âœ¨ å°†ä¸� [Roboflow](https://roboflow.com/?ref=ultralytics) çš„ [Joseph Nelson](https://github.com/josephofiowa) åœ¨ **æ¬§æ´²ä¸­éƒ¨æ—¶é—´ 12 æœˆ 13 æ—¥æ˜ŸæœŸäºŒçš„ 19:00** ï¼Œä»–å°†ä¸�æˆ‘ä»¬ä¸€èµ·è®¨è®ºå…¨æ–°çš„ Roboflow x Ultralytics HUB é›†æˆ�ã€‚æ¬¢è¿�æ”¶å�¬ Glenn å’Œ Joseph ï¼Œä»¥äº†è§£å¦‚ä½•é€šè¿‡æ— ç¼�æ•°æ�®é›†é›†æˆ�æ�¥åŠ å¿«å·¥ä½œæµ�ç¨‹ï¼� ğŸ”¥

<a align="center" href="https://youtu.be/QGRtEG7UjtE" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/85292283/205996456-bf3efa33-9c46-455e-b322-a64886cc7a0b.png"></a>
</div>

<h2 id="aa34aa2-a-a"><div align="center">å®�ä¾‹åˆ†å‰²æ¨¡å�‹ â­�  æ–°</div></h2>
<div align="center">
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
</div>

<p>æˆ‘ä»¬æ–°çš„ YOLOv5 <a href="https://github.com/ultralytics/yolov5/releases/v7.0">release v7.0</a> å®�ä¾‹åˆ†å‰²æ¨¡å�‹æ˜¯ä¸–ç•Œä¸Šæœ€å¿«å’Œæœ€å‡†ç¡®çš„æ¨¡å�‹ï¼Œå‡»è´¥æ‰€æœ‰å½“å‰� <a href="https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco">SOTA åŸºå‡†</a>ã€‚æˆ‘ä»¬ä½¿å®ƒé��å¸¸æ˜“äº�è®­ç»ƒã€�éªŒè¯�å’Œéƒ¨ç½²ã€‚æ›´å¤šç»†èŠ‚è¯·æŸ¥çœ‹ <a href="https://github.com/ultralytics/yolov5/releases/v7.0">å�‘è¡Œè¯´æ˜�</a> æˆ–è®¿é—®æˆ‘ä»¬çš„ <a href="https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb">YOLOv5 åˆ†å‰² Colab ç¬”è®°æœ¬</a> ä»¥å¿«é€Ÿå…¥é—¨ã€‚</p>
<details>
  <summary>å®�ä¾‹åˆ†å‰²æ¨¡å�‹åˆ—è¡¨</summary>

<br>

æˆ‘ä»¬ä½¿ç”¨ A100 GPU åœ¨ COCO ä¸Šä»¥ 640 å›¾åƒ�å¤§å°�è®­ç»ƒäº† 300 epochs å¾—åˆ° YOLOv5 åˆ†å‰²æ¨¡å�‹ã€‚æˆ‘ä»¬å°†æ‰€æœ‰æ¨¡å�‹å¯¼å‡ºåˆ° ONNX FP32 ä»¥è¿›è¡Œ CPU é€Ÿåº¦æµ‹è¯•ï¼Œå¹¶å¯¼å‡ºåˆ° TensorRT FP16 ä»¥è¿›è¡Œ GPU é€Ÿåº¦æµ‹è¯•ã€‚ä¸ºäº†ä¾¿äº�å†�ç�°ï¼Œæˆ‘ä»¬åœ¨ Google [Colab Pro](https://colab.research.google.com/signup) ä¸Šè¿›è¡Œäº†æ‰€æœ‰é€Ÿåº¦æµ‹è¯•ã€‚

| æ¨¡å�‹                                                                                        | å°ºå¯¸<br><sup>ï¼ˆåƒ�ç´ ï¼‰ | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | è®­ç»ƒæ—¶é•¿<br><sup>300 epochs<br>A100 GPUï¼ˆå°�æ—¶ï¼‰ | æ�¨ç�†é€Ÿåº¦<br><sup>ONNX CPU<br>ï¼ˆmsï¼‰ | æ�¨ç�†é€Ÿåº¦<br><sup>TRT A100<br>ï¼ˆmsï¼‰ | å�‚æ•°é‡�<br><sup>(M) | FLOPs<br><sup>@640 (B) |
| ------------------------------------------------------------------------------------------ | ------------------- | -------------------- | --------------------- | --------------------------------------------- | --------------------------------- | --------------------------------- | ----------------- | ---------------------- |
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640                 | 27.6                 | 23.4                  | 80:17                                         | **62.7**                          | **1.2**                           | **2.0**           | **7.1**                |
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640                 | 37.6                 | 31.7                  | 88:16                                         | 173.3                             | 1.4                               | 7.6               | 26.4                   |
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640                 | 45.0                 | 37.1                  | 108:36                                        | 427.0                             | 2.2                               | 22.0              | 70.8                   |
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640                 | 49.0                 | 39.9                  | 66:43 (2x)                                    | 857.4                             | 2.9                               | 47.9              | 147.7                  |
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640                 | **50.7**             | **41.4**              | 62:56 (3x)                                    | 1579.2                            | 4.5                               | 88.8              | 265.7                  |

-   æ‰€æœ‰æ¨¡å�‹ä½¿ç”¨ SGD ä¼˜åŒ–å™¨è®­ç»ƒï¼Œ éƒ½ä½¿ç”¨ `lr0=0.01` å’Œ `weight_decay=5e-5` å�‚æ•°ï¼Œ å›¾åƒ�å¤§å°�ä¸º 640 ã€‚<br>è®­ç»ƒ log å�¯ä»¥æŸ¥çœ‹ https://wandb.ai/glenn-jocher/YOLOv5_v70_official
-   **å‡†ç¡®æ€§**ç»“æ�œéƒ½åœ¨ COCO æ•°æ�®é›†ä¸Šï¼Œä½¿ç”¨å�•æ¨¡å�‹å�•å°ºåº¦æµ‹è¯•å¾—åˆ°ã€‚<br>å¤�ç�°å‘½ä»¤ `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
-   **æ�¨ç�†é€Ÿåº¦**æ˜¯ä½¿ç”¨ 100 å¼ å›¾åƒ�æ�¨ç�†æ—¶é—´è¿›è¡Œå¹³å�‡å¾—åˆ°ï¼Œæµ‹è¯•ç�¯å¢ƒä½¿ç”¨ [Colab Pro](https://colab.research.google.com/signup) ä¸Š A100 é«˜ RAM å®�ä¾‹ã€‚ç»“æ�œä»…è¡¨ç¤ºæ�¨ç�†é€Ÿåº¦ï¼ˆNMS æ¯�å¼ å›¾åƒ�å¢�åŠ çº¦ 1 æ¯«ç§’ï¼‰ã€‚<br>å¤�ç�°å‘½ä»¤ `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
-   **æ¨¡å�‹è½¬æ�¢**åˆ° FP32 çš„ ONNX å’Œ FP16 çš„ TensorRT è„šæœ¬ä¸º `export.py`.<br>è¿�è¡Œå‘½ä»¤ `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`

</details>

<details>
  <summary>åˆ†å‰²æ¨¡å�‹ä½¿ç”¨ç¤ºä¾‹  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>

### è®­ç»ƒ

YOLOv5åˆ†å‰²è®­ç»ƒæ”¯æŒ�è‡ªåŠ¨ä¸‹è½½ COCO128-seg åˆ†å‰²æ•°æ�®é›†ï¼Œç”¨æˆ·ä»…éœ€åœ¨å�¯åŠ¨æŒ‡ä»¤ä¸­åŒ…å�« `--data coco128-seg.yaml` å�‚æ•°ã€‚ è‹¥è¦�æ‰‹åŠ¨ä¸‹è½½ï¼Œä½¿ç”¨å‘½ä»¤ `bash data/scripts/get_coco.sh --train --val --segments`ï¼Œ åœ¨ä¸‹è½½å®Œæ¯•å��ï¼Œä½¿ç”¨å‘½ä»¤ `python train.py --data coco.yaml` å¼€å�¯è®­ç»ƒã€‚

```bash
# å�• GPU
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640

# å¤š GPUï¼Œ DDP æ¨¡å¼�
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
```

### éªŒè¯�

åœ¨ COCO æ•°æ�®é›†ä¸ŠéªŒè¯� YOLOv5s-seg mask mAPï¼š

```bash
bash data/scripts/get_coco.sh --val --segments  # ä¸‹è½½ COCO val segments æ•°æ�®é›† (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # éªŒè¯�
```

### é¢„æµ‹

ä½¿ç”¨é¢„è®­ç»ƒçš„ YOLOv5m-seg.pt æ�¥é¢„æµ‹ bus.jpgï¼š

```bash
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
```

```python
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt')  # ä»�load from PyTorch Hub åŠ è½½æ¨¡å�‹ (WARNING: æ�¨ç�†æš‚æœªæ”¯æŒ�)
```

| ![zidane](https://user-images.githubusercontent.com/26833433/203113421-decef4c4-183d-4a0a-a6c2-6435b33bc5d3.jpg) | ![bus](https://user-images.githubusercontent.com/26833433/203113416-11fe0025-69f7-4874-a0a6-65d0bfe2999a.jpg) |
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |

### æ¨¡å�‹å¯¼å‡º

å°† YOLOv5s-seg æ¨¡å�‹å¯¼å‡ºåˆ° ONNX å’Œ TensorRTï¼š

```bash
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
```

</details>

<h2 id="_1"><div align="center">æ–‡æ¡£</div></h2>
<p>æœ‰å…³è®­ç»ƒã€�æµ‹è¯•å’Œéƒ¨ç½²çš„å®Œæ•´æ–‡æ¡£è§�<a href="https://docs.ultralytics.com">YOLOv5 æ–‡æ¡£</a>ã€‚è¯·å�‚é˜…ä¸‹é�¢çš„å¿«é€Ÿå…¥é—¨ç¤ºä¾‹ã€‚</p>
<details open>
<summary>å®‰è£…</summary>

å…‹éš† repoï¼Œå¹¶è¦�æ±‚åœ¨ [**Python>=3.7.0**](https://www.python.org/) ç�¯å¢ƒä¸­å®‰è£… [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) ï¼Œä¸”è¦�æ±‚ [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/) ã€‚

```bash
git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install
```

</details>

<details>
<summary>æ�¨ç�†</summary>

ä½¿ç”¨ YOLOv5 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) æ�¨ç�†ã€‚æœ€æ–° [æ¨¡å�‹](https://github.com/ultralytics/yolov5/tree/master/models) å°†è‡ªåŠ¨çš„ä»�
YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) ä¸­ä¸‹è½½ã€‚

```python
import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5n - yolov5x6, custom

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
```

</details>

<details>
<summary>ä½¿ç”¨ detect.py æ�¨ç�†</summary>

`detect.py` åœ¨å�„ç§�æ�¥æº�ä¸Šè¿�è¡Œæ�¨ç�†ï¼Œ [æ¨¡å�‹](https://github.com/ultralytics/yolov5/tree/master/models) è‡ªåŠ¨ä»�
æœ€æ–°çš„YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) ä¸­ä¸‹è½½ï¼Œå¹¶å°†ç»“æ�œä¿�å­˜åˆ° `runs/detect` ã€‚

```bash
python detect.py --weights yolov5s.pt --source 0                               # webcam
                                               img.jpg                         # image
                                               vid.mp4                         # video
                                               screen                          # screenshot
                                               path/                           # directory
                                               list.txt                        # list of images
                                               list.streams                    # list of streams
                                               'path/*.jpg'                    # glob
                                               'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                               'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
```

</details>

<details>
<summary>è®­ç»ƒ</summary>

ä¸‹é�¢çš„å‘½ä»¤é‡�ç�° YOLOv5 åœ¨ [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) æ•°æ�®é›†ä¸Šçš„ç»“æ�œã€‚
æœ€æ–°çš„ [æ¨¡å�‹](https://github.com/ultralytics/yolov5/tree/master/models) å’Œ [æ•°æ�®é›†](https://github.com/ultralytics/yolov5/tree/master/data)
å°†è‡ªåŠ¨çš„ä»� YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) ä¸­ä¸‹è½½ã€‚
YOLOv5n/s/m/l/x åœ¨ V100 GPU çš„è®­ç»ƒæ—¶é—´ä¸º 1/2/4/6/8 å¤©ï¼ˆ [å¤šGPU](https://github.com/ultralytics/yolov5/issues/475) è®­ç»ƒé€Ÿåº¦æ›´å¿«ï¼‰ã€‚
å°½å�¯èƒ½ä½¿ç”¨æ›´å¤§çš„ `--batch-size` ï¼Œæˆ–é€šè¿‡ `--batch-size -1` å®�ç�°
YOLOv5 [è‡ªåŠ¨æ‰¹å¤„ç�†](https://github.com/ultralytics/yolov5/pull/5092) ã€‚ä¸‹æ–¹æ˜¾ç¤ºçš„ batchsize é€‚ç”¨äº� V100-16GBã€‚

```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml  --batch-size 128
                                                                 yolov5s                    64
                                                                 yolov5m                    40
                                                                 yolov5l                    24
                                                                 yolov5x                    16
```

<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">

</details>

<details open>
<summary>æ•™ç¨‹</summary>

-   [è®­ç»ƒè‡ªå®šä¹‰æ•°æ�®](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)ğŸš€ æ�¨è��
-   [è�·å¾—æœ€ä½³è®­ç»ƒç»“æ�œçš„æŠ€å·§](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)â˜˜ï¸� æ�¨è��
-   [å¤š GPU è®­ç»ƒ](https://github.com/ultralytics/yolov5/issues/475)
-   [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)ğŸŒŸ æ–°
-   [TFLiteã€�ONNXã€�CoreMLã€�TensorRT å¯¼å‡º](https://github.com/ultralytics/yolov5/issues/251)ğŸš€
-   [NVIDIA Jetson Nano éƒ¨ç½²](https://github.com/ultralytics/yolov5/issues/9627)ğŸŒŸ æ–°
-   [æµ‹è¯•æ—¶æ•°æ�®å¢�å¼º (TTA)](https://github.com/ultralytics/yolov5/issues/303)
-   [æ¨¡å�‹é›†æˆ�](https://github.com/ultralytics/yolov5/issues/318)
-   [æ¨¡å�‹ä¿®å‰ª/ç¨€ç–�åº¦](https://github.com/ultralytics/yolov5/issues/304)
-   [è¶…å�‚æ•°è¿›åŒ–](https://github.com/ultralytics/yolov5/issues/607)
-   [ä½¿ç”¨å†»ç»“å±‚è¿›è¡Œè¿�ç§»å­¦ä¹ ](https://github.com/ultralytics/yolov5/issues/1314)
-   [æ�¶æ�„æ€»ç»“](https://github.com/ultralytics/yolov5/issues/6998)ğŸŒŸ æ–°
-   [ç”¨äº�æ•°æ�®é›†ã€�æ ‡ç­¾å’Œä¸»åŠ¨å­¦ä¹ çš„ Roboflow](https://github.com/ultralytics/yolov5/issues/4975)ğŸŒŸ æ–°
-   [ClearML è®°å½•](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml)ğŸŒŸ æ–°
-   [Deci å¹³å�°](https://github.com/ultralytics/yolov5/wiki/Deci-Platform)ğŸŒŸ æ–°
-   [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet)ğŸŒŸ æ–°

</details>

<h2 id="ae"><div align="center">æ¨¡å�—é›†æˆ�</div></h2>
<p><br>
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png"></a>
<br>
<br></p>
<div align="center">
  <a href="https://roboflow.com/?ref=ultralytics">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
  <a href="https://cutt.ly/yolov5-readme-clearml">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-clearml.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
  <a href="https://bit.ly/yolov5-readme-comet">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-comet.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="15%" height="0" alt="" />
  <a href="https://bit.ly/yolov5-deci-platform">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-deci.png" width="10%" /></a>
</div>

<table>
<thead>
<tr>
<th align="center">Roboflow</th>
<th align="center">ClearML â­� æ–°</th>
<th align="center">Comet â­� æ–°</th>
<th align="center">Deci â­� æ–°</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">å°†æ‚¨çš„è‡ªå®šä¹‰æ•°æ�®é›†è¿›è¡Œæ ‡æ³¨å¹¶ç›´æ�¥å¯¼å‡ºåˆ° YOLOv5 ä»¥è¿›è¡Œè®­ç»ƒ <a href="https://roboflow.com/?ref=ultralytics">Roboflow</a></td>
<td align="center">è‡ªåŠ¨è·Ÿè¸ªã€�å�¯è§†åŒ–ç”šè‡³è¿œç¨‹è®­ç»ƒ YOLOv5 <a href="https://cutt.ly/yolov5-readme-clearml">ClearML</a>ï¼ˆå¼€æº�ï¼�ï¼‰</td>
<td align="center">æ°¸è¿œå…�è´¹ï¼Œ<a href="https://bit.ly/yolov5-readme-comet">Comet</a>å�¯è®©æ‚¨ä¿�å­˜ YOLOv5 æ¨¡å�‹ã€�æ�¢å¤�è®­ç»ƒä»¥å�Šäº¤äº’å¼�å�¯è§†åŒ–å’Œè°ƒè¯•é¢„æµ‹</td>
<td align="center">ä¸€é”®è‡ªåŠ¨ç¼–è¯‘é‡�åŒ– YOLOv5 ä»¥è�·å¾—æ›´å¥½çš„æ�¨ç�†æ€§èƒ½<a href="https://bit.ly/yolov5-deci-platform">Deci</a></td>
</tr>
</tbody>
</table>
<h2 id="ultralytics-hub"><div align="center">Ultralytics HUB</div></h2>
<p><a href="https://bit.ly/ultralytics_hub">Ultralytics HUB</a> æ˜¯æˆ‘ä»¬çš„â­�<strong>æ–°çš„</strong>ç”¨äº�å�¯è§†åŒ–æ•°æ�®é›†ã€�è®­ç»ƒ YOLOv5 ğŸš€ æ¨¡å�‹å¹¶ä»¥æ— ç¼�ä½“éªŒéƒ¨ç½²åˆ°ç�°å®�ä¸–ç•Œçš„æ— ä»£ç �è§£å†³æ–¹æ¡ˆã€‚ç�°åœ¨å¼€å§‹ <strong>å…�è´¹</strong> ä½¿ç”¨ä»–ï¼�</p>
<p><a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/master/im/ultralytics-hub.png"></a></p>
<h2 id="a-oaa1e-yolov5"><div align="center">ä¸ºä»€ä¹ˆé€‰æ‹© YOLOv5</div></h2>
<p>YOLOv5 è¶…çº§å®¹æ˜“ä¸Šæ‰‹ï¼Œç®€å�•æ˜“å­¦ã€‚æˆ‘ä»¬ä¼˜å…ˆè€ƒè™‘ç�°å®�ä¸–ç•Œçš„ç»“æ�œã€‚</p>
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>

<details>
  <summary>YOLOv5-P5 640 å›¾</summary>

<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
</details>

<details>
  <summary>å›¾è¡¨ç¬”è®°</summary>

-   **COCO AP val** è¡¨ç¤º mAP@0.5:0.95 æŒ‡æ ‡ï¼Œåœ¨ [COCO val2017](http://cocodataset.org) æ•°æ�®é›†çš„ 5000 å¼ å›¾åƒ�ä¸Šæµ‹å¾—ï¼Œ å›¾åƒ�åŒ…å�« 256 åˆ° 1536 å�„ç§�æ�¨ç�†å¤§å°�ã€‚
-   **æ˜¾å�¡æ�¨ç�†é€Ÿåº¦** ä¸ºåœ¨ [COCO val2017](http://cocodataset.org) æ•°æ�®é›†ä¸Šçš„å¹³å�‡æ�¨ç�†æ—¶é—´ï¼Œä½¿ç”¨ [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100å®�ä¾‹ï¼Œbatchsize ä¸º 32 ã€‚
-   **EfficientDet** æ•°æ�®æ�¥è‡ª [google/automl](https://github.com/google/automl) ï¼Œ batchsize ä¸º32ã€‚
-   **å¤�ç�°å‘½ä»¤** ä¸º `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`

</details>

<h3 id="eec-a">é¢„è®­ç»ƒæ¨¡å�‹</h3>
<table>
<thead>
<tr>
<th>æ¨¡å�‹</th>
<th>å°ºå¯¸<br><sup>ï¼ˆåƒ�ç´ ï¼‰</th>
<th>mAP<sup>val<br>50-95</th>
<th>mAP<sup>val<br>50</th>
<th>æ�¨ç�†é€Ÿåº¦<br><sup>CPU b1<br>ï¼ˆmsï¼‰</th>
<th>æ�¨ç�†é€Ÿåº¦<br><sup>V100 b1<br>ï¼ˆmsï¼‰</th>
<th>é€Ÿåº¦<br><sup>V100 b32<br>ï¼ˆmsï¼‰</th>
<th>å�‚æ•°é‡�<br><sup>(M)</th>
<th>FLOPs<br><sup>@640 (B)</th>
</tr>
</thead>
<tbody>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt">YOLOv5n</a></td>
<td>640</td>
<td>28.0</td>
<td>45.7</td>
<td><strong>45</strong></td>
<td><strong>6.3</strong></td>
<td><strong>0.6</strong></td>
<td><strong>1.9</strong></td>
<td><strong>4.5</strong></td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt">YOLOv5s</a></td>
<td>640</td>
<td>37.4</td>
<td>56.8</td>
<td>98</td>
<td>6.4</td>
<td>0.9</td>
<td>7.2</td>
<td>16.5</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt">YOLOv5m</a></td>
<td>640</td>
<td>45.4</td>
<td>64.1</td>
<td>224</td>
<td>8.2</td>
<td>1.7</td>
<td>21.2</td>
<td>49.0</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt">YOLOv5l</a></td>
<td>640</td>
<td>49.0</td>
<td>67.3</td>
<td>430</td>
<td>10.1</td>
<td>2.7</td>
<td>46.5</td>
<td>109.1</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt">YOLOv5x</a></td>
<td>640</td>
<td>50.7</td>
<td>68.9</td>
<td>766</td>
<td>12.1</td>
<td>4.8</td>
<td>86.7</td>
<td>205.7</td>
</tr>
<tr>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n6.pt">YOLOv5n6</a></td>
<td>1280</td>
<td>36.0</td>
<td>54.4</td>
<td>153</td>
<td>8.1</td>
<td>2.1</td>
<td>3.2</td>
<td>4.6</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s6.pt">YOLOv5s6</a></td>
<td>1280</td>
<td>44.8</td>
<td>63.7</td>
<td>385</td>
<td>8.2</td>
<td>3.6</td>
<td>12.6</td>
<td>16.8</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m6.pt">YOLOv5m6</a></td>
<td>1280</td>
<td>51.3</td>
<td>69.3</td>
<td>887</td>
<td>11.1</td>
<td>6.8</td>
<td>35.7</td>
<td>50.0</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l6.pt">YOLOv5l6</a></td>
<td>1280</td>
<td>53.7</td>
<td>71.3</td>
<td>1784</td>
<td>15.8</td>
<td>10.5</td>
<td>76.8</td>
<td>111.4</td>
</tr>
<tr>
<td><a href="https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt">YOLOv5x6</a><br>+<a href="https://github.com/ultralytics/yolov5/issues/303">TTA</a></td>
<td>1280<br>1536</td>
<td>55.0<br><strong>55.8</strong></td>
<td>72.7<br><strong>72.7</strong></td>
<td>3136<br>-</td>
<td>26.2<br>-</td>
<td>19.4<br>-</td>
<td>140.7<br>-</td>
<td>209.8<br>-</td>
</tr>
</tbody>
</table>
<details>
  <summary>ç¬”è®°</summary>

-   æ‰€æœ‰æ¨¡å�‹éƒ½ä½¿ç”¨é»˜è®¤é…�ç½®ï¼Œè®­ç»ƒ 300 epochsã€‚nå’Œsæ¨¡å�‹ä½¿ç”¨ [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) ï¼Œå…¶ä»–æ¨¡å�‹éƒ½ä½¿ç”¨ [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml) ã€‚
-   **mAP<sup>val</sup>**åœ¨å�•æ¨¡å�‹å�•å°ºåº¦ä¸Šè®¡ç®—ï¼Œæ•°æ�®é›†ä½¿ç”¨ [COCO val2017](http://cocodataset.org) ã€‚<br>å¤�ç�°å‘½ä»¤ `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-   **æ�¨ç�†é€Ÿåº¦**åœ¨ COCO val å›¾åƒ�æ€»ä½“æ—¶é—´ä¸Šè¿›è¡Œå¹³å�‡å¾—åˆ°ï¼Œæµ‹è¯•ç�¯å¢ƒä½¿ç”¨[AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/)å®�ä¾‹ã€‚ NMS æ—¶é—´ (å¤§çº¦ 1 ms/img) ä¸�åŒ…æ‹¬åœ¨å†…ã€‚<br>å¤�ç�°å‘½ä»¤ `python val.py --data coco.yaml --img 640 --task speed --batch 1`
-   **TTA** [æµ‹è¯•æ—¶æ•°æ�®å¢�å¼º](https://github.com/ultralytics/yolov5/issues/303) åŒ…æ‹¬å��å°„å’Œå°ºåº¦å�˜æ�¢ã€‚<br>å¤�ç�°å‘½ä»¤ `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`

</details>

<h2 id="acc12c-a"><div align="center">åˆ†ç±»ç½‘ç»œ â­� æ–°</div></h2>
<p>YOLOv5 <a href="https://github.com/ultralytics/yolov5/releases">release v6.2</a> å¸¦æ�¥å¯¹åˆ†ç±»æ¨¡å�‹è®­ç»ƒã€�éªŒè¯�å’Œéƒ¨ç½²çš„æ”¯æŒ�ï¼�è¯¦æƒ…è¯·æŸ¥çœ‹ <a href="https://github.com/ultralytics/yolov5/releases/v6.2">å�‘è¡Œè¯´æ˜�</a> æˆ–è®¿é—®æˆ‘ä»¬çš„ <a href="https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb">YOLOv5 åˆ†ç±» Colab ç¬”è®°æœ¬</a> ä»¥å¿«é€Ÿå…¥é—¨ã€‚</p>
<details>
  <summary>åˆ†ç±»ç½‘ç»œæ¨¡å�‹</summary>

<br>

æˆ‘ä»¬ä½¿ç”¨ 4xA100 å®�ä¾‹åœ¨ ImageNet ä¸Šè®­ç»ƒäº† 90 ä¸ª epochs å¾—åˆ° YOLOv5-cls åˆ†ç±»æ¨¡å�‹ï¼Œæˆ‘ä»¬è®­ç»ƒäº† ResNet å’Œ EfficientNet æ¨¡å�‹ä»¥å�Šç›¸å�Œçš„é»˜è®¤è®­ç»ƒè®¾ç½®ä»¥è¿›è¡Œæ¯”è¾ƒã€‚æˆ‘ä»¬å°†æ‰€æœ‰æ¨¡å�‹å¯¼å‡ºåˆ° ONNX FP32 ä»¥è¿›è¡Œ CPU é€Ÿåº¦æµ‹è¯•ï¼Œå¹¶å¯¼å‡ºåˆ° TensorRT FP16 ä»¥è¿›è¡Œ GPU é€Ÿåº¦æµ‹è¯•ã€‚ä¸ºäº†ä¾¿äº�é‡�ç�°ï¼Œæˆ‘ä»¬åœ¨ Google ä¸Šè¿›è¡Œäº†æ‰€æœ‰é€Ÿåº¦æµ‹è¯• [Colab Pro](https://colab.research.google.com/signup) ã€‚

| æ¨¡å�‹                                                                                                | å°ºå¯¸<br><sup>ï¼ˆåƒ�ç´ ï¼‰ | acc<br><sup>top1 | acc<br><sup>top5 | è®­ç»ƒæ—¶é•¿<br><sup>90 epochs<br>4xA100ï¼ˆå°�æ—¶ï¼‰ | æ�¨ç�†é€Ÿåº¦<br><sup>ONNX CPU<br>ï¼ˆmsï¼‰ | æ�¨ç�†é€Ÿåº¦<br><sup>TensorRT V100<br>ï¼ˆmsï¼‰ | å�‚æ•°<br><sup>(M) | FLOPs<br><sup>@640 (B) |
| -------------------------------------------------------------------------------------------------- | ------------------- | ---------------- | ---------------- | ------------------------------------------ | --------------------------------- | -------------------------------------- | --------------- | -----------------------|
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n-cls.pt)         | 224                 | 64.6             | 85.4             | 7:59                                       | **3.3**                           | **0.5**                                | **2.5**         | **0.5**                |
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s-cls.pt)         | 224                 | 71.5             | 90.2             | 8:09                                       | 6.6                               | 0.6                                    | 5.4             | 1.4                    |
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m-cls.pt)         | 224                 | 75.9             | 92.9             | 10:06                                      | 15.5                              | 0.9                                    | 12.9            | 3.9                    |
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l-cls.pt)         | 224                 | 78.0             | 94.0             | 11:56                                      | 26.9                              | 1.4                                    | 26.5            | 8.5                    |
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x-cls.pt)         | 224                 | **79.0**         | **94.4**         | 15:04                                      | 54.3                              | 1.8                                    | 48.1            | 15.9                   |
|                                                                                                    |                     |                  |                  |                                            |                                   |                                        |                 |                        |
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet18.pt)               | 224                 | 70.3             | 89.5             | **6:47**                                   | 11.2                              | 0.5                                    | 11.7            | 3.7                    |
| [Resnetzch](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet34.pt)              | 224                 | 73.9             | 91.8             | 8:33                                       | 20.6                              | 0.9                                    | 21.8            | 7.4                    |
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet50.pt)               | 224                 | 76.8             | 93.4             | 11:10                                      | 23.4                              | 1.0                                    | 25.6            | 8.5                    |
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v6.2/resnet101.pt)             | 224                 | 78.5             | 94.3             | 17:10                                      | 42.1                              | 1.9                                    | 44.5            | 15.9                   |
|                                                                                                    |                     |                  |                  |                                            |                                   |                                        |                 |                        |
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b0.pt) | 224                 | 75.1             | 92.4             | 13:03                                      | 12.5                              | 1.3                                    | 5.3             | 1.0                    |
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b1.pt) | 224                 | 76.4             | 93.2             | 17:04                                      | 14.9                              | 1.6                                    | 7.8             | 1.5                    |
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b2.pt) | 224                 | 76.6             | 93.4             | 17:10                                      | 15.9                              | 1.6                                    | 9.1             | 1.7                    |
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v6.2/efficientnet_b3.pt) | 224                 | 77.7             | 94.0             | 19:19                                      | 18.9                              | 1.9                                    | 12.2            | 2.4                    |

<details>
  <summary>Table Notes (ç‚¹å‡»ä»¥å±•å¼€)</summary>

-   æ‰€æœ‰æ¨¡å�‹éƒ½ä½¿ç”¨ SGD ä¼˜åŒ–å™¨è®­ç»ƒ 90 ä¸ª epochsï¼Œéƒ½ä½¿ç”¨ `lr0=0.001` å’Œ `weight_decay=5e-5` å�‚æ•°ï¼Œ å›¾åƒ�å¤§å°�ä¸º 224 ï¼Œä¸”éƒ½ä½¿ç”¨é»˜è®¤è®¾ç½®ã€‚<br>è®­ç»ƒ log å�¯ä»¥æŸ¥çœ‹ https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
-   **å‡†ç¡®æ€§**éƒ½åœ¨å�•æ¨¡å�‹å�•å°ºåº¦ä¸Šè®¡ç®—ï¼Œæ•°æ�®é›†ä½¿ç”¨ [ImageNet-1k](https://www.image-net.org/index.php) ã€‚<br>å¤�ç�°å‘½ä»¤ `python classify/val.py --data ../datasets/imagenet --img 224`
-   **æ�¨ç�†é€Ÿåº¦**æ˜¯ä½¿ç”¨ 100 ä¸ªæ�¨ç�†å›¾åƒ�è¿›è¡Œå¹³å�‡å¾—åˆ°ï¼Œæµ‹è¯•ç�¯å¢ƒä½¿ç”¨è°·æ­Œ [Colab Pro](https://colab.research.google.com/signup) V100 é«˜ RAM å®�ä¾‹ã€‚<br>å¤�ç�°å‘½ä»¤ `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
-   **æ¨¡å�‹å¯¼å‡º**åˆ° FP32 çš„ ONNX å’Œ FP16 çš„ TensorRT ä½¿ç”¨ `export.py` ã€‚<br>å¤�ç�°å‘½ä»¤ `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
    </details>
    </details>

<details>
  <summary>åˆ†ç±»è®­ç»ƒç¤ºä¾‹  <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>

### è®­ç»ƒ

YOLOv5 åˆ†ç±»è®­ç»ƒæ”¯æŒ�è‡ªåŠ¨ä¸‹è½½ MNISTã€�Fashion-MNISTã€�CIFAR10ã€�CIFAR100ã€�Imagenetteã€�Imagewoof å’Œ ImageNet æ•°æ�®é›†ï¼Œå‘½ä»¤ä¸­ä½¿ç”¨ `--data` å�³å�¯ã€‚ MNIST ç¤ºä¾‹ `--data mnist` ã€‚

```bash
# å�• GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128

# å¤š GPUï¼Œ DDP æ¨¡å¼�
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
```

### éªŒè¯�

åœ¨ ImageNet-1k æ•°æ�®é›†ä¸ŠéªŒè¯� YOLOv5m-cls çš„å‡†ç¡®æ€§ï¼š

```bash
bash data/scripts/get_imagenet.sh --val  # download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224  # validate
```

### é¢„æµ‹

ä½¿ç”¨é¢„è®­ç»ƒçš„ YOLOv5s-cls.pt æ�¥é¢„æµ‹ bus.jpgï¼š

```bash
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
```

```python
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s-cls.pt')  # load from PyTorch Hub
```

### æ¨¡å�‹å¯¼å‡º

å°†ä¸€ç»„ç»�è¿‡è®­ç»ƒçš„ YOLOv5s-clsã€�ResNet å’Œ EfficientNet æ¨¡å�‹å¯¼å‡ºåˆ° ONNX å’Œ TensorRTï¼š

```bash
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
```

</details>

<h2 id="c-a"><div align="center">ç�¯å¢ƒ</div></h2>
<p>ä½¿ç”¨ä¸‹é�¢æˆ‘ä»¬ç»�è¿‡éªŒè¯�çš„ç�¯å¢ƒï¼Œåœ¨å‡ ç§’é’Ÿå†…å¼€å§‹ä½¿ç”¨ YOLOv5 ã€‚å�•å‡»ä¸‹é�¢çš„å›¾æ ‡äº†è§£è¯¦ç»†ä¿¡æ�¯ã€‚</p>
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  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
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    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
  <a href="https://www.kaggle.com/ultralytics/yolov5">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
  <a href="https://hub.docker.com/r/ultralytics/yolov5">
    <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="10%" /></a>
  <img src="https://github.com/ultralytics/assets/raw/master/social/logo-transparent.png" width="5%" alt="" />
  <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
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  <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
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</div>

<h2 id="app"><div align="center">APP</div></h2>
<p>é€šè¿‡ä¸‹è½½ <a href="https://ultralytics.com/app_install">Ultralytics APP</a> ï¼Œä»¥åœ¨æ‚¨çš„ iOS æˆ– Android è®¾å¤‡ä¸Šè¿�è¡Œ YOLOv5 æ¨¡å�‹!</p>
<p><a align="center" href="https://ultralytics.com/app_install" target="_blank">
<img width="100%" alt="Ultralytics mobile app" src="https://user-images.githubusercontent.com/26833433/202829285-39367043-292a-41eb-bb76-c3e74f38e38e.png"></p>
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<p>æˆ‘ä»¬å–œæ¬¢æ‚¨çš„æ„�è§�æˆ–å»ºè®®ï¼�æˆ‘ä»¬å¸Œæœ›å°½å�¯èƒ½ç®€å�•å’Œé€�æ˜�åœ°ä¸º YOLOv5 å�šå‡ºè´¡çŒ®ã€‚è¯·çœ‹æˆ‘ä»¬çš„ <a href="CONTRIBUTING.md">æŠ•ç¨¿æŒ‡å�—</a>ï¼Œå¹¶å¡«å†™ <a href="https://ultralytics.com/survey?utm_source=github&amp;utm_medium=social&amp;utm_campaign=Survey">YOLOv5è°ƒæŸ¥</a> å�‘æˆ‘ä»¬å�‘é€�æ‚¨çš„ä½“éªŒå��é¦ˆã€‚æ„Ÿè°¢æˆ‘ä»¬æ‰€æœ‰çš„è´¡çŒ®è€…ï¼�</p>
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<h2 id="license"><div align="center">License</div></h2>
<p>YOLOv5 åœ¨ä¸¤ç§�ä¸�å�Œçš„ License ä¸‹å�¯ç”¨ï¼š</p>
<ul>
<li><strong>GPL-3.0 License</strong>ï¼š æŸ¥çœ‹ <a href="https://github.com/ultralytics/yolov5/blob/master/LICENSE">License</a> æ–‡ä»¶çš„è¯¦ç»†ä¿¡æ�¯ã€‚</li>
<li><strong>ä¼�ä¸šLicense</strong>ï¼šåœ¨æ²¡æœ‰ GPL-3.0 å¼€æº�è¦�æ±‚çš„æƒ…å†µä¸‹ä¸ºå•†ä¸šäº§å“�å¼€å�‘æ��ä¾›æ›´å¤§çš„ç�µæ´»æ€§ã€‚å…¸å�‹ç”¨ä¾‹æ˜¯å°† Ultralytics è½¯ä»¶å’Œ AI æ¨¡å�‹åµŒå…¥åˆ°å•†ä¸šäº§å“�å’Œåº”ç”¨ç¨‹åº�ä¸­ã€‚åœ¨ä»¥ä¸‹ä½�ç½®ç”³è¯·ä¼�ä¸šè®¸å�¯è¯� <a href="https://ultralytics.com/license">Ultralytics è®¸å�¯</a> ã€‚</li>
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<p>è‹¥å�‘ç�° YOLOv5 çš„ bug æˆ–æœ‰åŠŸèƒ½éœ€æ±‚ï¼Œè¯·è®¿é—® <a href="https://github.com/ultralytics/yolov5/issues">GitHub é—®é¢˜</a> ã€‚å¦‚éœ€ä¸“ä¸šæ”¯æŒ�ï¼Œè¯· <a href="https://ultralytics.com/contact">è�”ç³»æˆ‘ä»¬</a> ã€‚</p>
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